Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) <doi:10.48550/arXiv.2010.03567>), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles.

The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.

Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time. For details, see Fay and Follmann, 2016 <DOI:10.1177/1740774516654861>.

National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.

This package performs network meta-analysis using integrated nested Laplace approximations ('INLA') which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis dataset. INLA package can be obtained from <https://www.r-inla.org>.

The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the best fit on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) <doi:10.1109/ACCESS.2013.2267611>.

This package provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created.

Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.

This package provides an htmlwidgets <https://www.htmlwidgets.org/> interface to NGL.js <http://nglviewer.org/ngl/api/>. NGLvieweR can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications. It includes a set of API functions to manipulate the viewer after creation in Shiny.

This package provides tools for non-parametric Fourier deconvolution using the N-Power Fourier Deconvolution (NPFD) method. This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets.

We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.

Macros to generate nimble code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the nimble manual: <https://r-nimble.org/html_manual/cha-writing-models.html#subsec:macros>.

Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) <doi:10.48550/arXiv.2507.05806>.

Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three shiny tools, one to simulate insurance claims applying reinsurance structures, fit generalised linear models and fit claims frequency or severity distributions. Methods used in the package refer to Free for All by Yiannis Parizas (2023) <https://www.theactuary.com/2023/03/02/free-all>; Escaping the triangle by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/06/2019/06/05/escaping-triangle>; Take to excess by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/03/2019/03/06/taken-excess>.

This package provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, through the use of Stan and glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) <doi:10.1080/01621459.2023.2165929>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.

Estimators and variance estimators tailored to the NILS hierarchical design (Adler et al. 2020, <https://res.slu.se/id/publ/105630>; Grafström et al. 2023, <https://res.slu.se/id/publ/128235>). The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats <https://www.slu.se/om-slu/organisation/institutioner/skoglig-resurshushallning/miljoanalys/nils/>.

Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.

Allele frequency databases for 50 forensic short tandem repeat (STR) markers, covering Norway and several broader regional populations: Europe, Africa, South America, West Asia, Middle Asia, and East Asia. Developed and maintained for use at the Department of Forensic Sciences, Oslo, Norway.

Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic (2019) <doi:10.1016/j.yrtph.2019.104418>.

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) <doi:10.31234/osf.io/cz32t>.

This package provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon igraph'.

This package provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.

Format numbers and plots for publication; includes the removal of leading zeros, standardization of number of digits, addition of affixes, and a p-value formatter. These tools combine the functionality of several base functions such as paste()', format()', and sprintf() into specific use case functions that are named in a way that is consistent with usage, making their names easy to remember and easy to deploy.